An Investigation into the Structural Basis for Nucleic Acid Small Molecule Binding
Bibliographic record
Abstract
The past 30 years of RNA research have seen a fundamental shift in our understanding of the biological roles that these macromolecules play.The sea change from our initial conception of RNA as a mere messenger of genetic information between DNA and proteins to our current understanding of RNA as a key player in various genetic and metabolic roles through their non-coding counterparts has motivated attempts to elucidate the structural underpinnings of their various biological functions.In this thesis, I describe research to develop and implement methods that formally represent nucleic acid structure, query and reason over their properties and computationally identify RNA structural motifs that are predictive of ligand binding.Chapter 1 presents the motivation, overall hypothesis and main objectives for this doctoral research, as well as a brief overview of the principles of nucleic acid structure and their representation using Semantic Web technologies.In Chapter 2, I present the RNA Knowledge Base (RKB), an instantiated ontology about RNA structure that provides machine understandable descriptions of nucleotide base pairs as observed in solved 3D structures.In Chapter 3, I describe Aptamer Base, a collaborative online knowledge base to describe aptamers and the details of the SELEX experiments that created them.In Chapter 4, I describe a methodology and implementation for the computational extraction of RNA motifs from a graph representation of their structures, and demonstrate that features of these motifs are predictive of ligand binding.In Chapter 5, I discuss future directions and present a summary of the contributions of this thesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".